code: def __init__(self, mu, sigma, sigma_min, n_steps_annealing):
      self.mu = mu
      self.sigma = sigma
      self.n_steps = 0
  
      if sigma_min is not None:
          self.m = -float(sigma - sigma_min) / float(n_steps_annealing)
          self.c = sigma
          self.sigma_min = sigma_min
      else:
          self.m = 0.0
          self.c = sigma
          self.sigma_min = sigma
  
file_path: keras-rl/rl/random.py
imports: from __future__ import division
  import numpy
label_desc: Use image node_id 1 to create a new AnnealedGaussianProcess object from inherited base classes: RandomProcess with example: obj = AnnealedGaussianProcess(mu, sigma, sigma_min, n_steps_annealing)